In [1]:
%matplotlib inline
import pandas as pd
import seaborn as sns
import plotly.express as px

import matplotlib.pyplot as plt

E:\Anaconda3\envs\TIL6022\lib\site-packages\scipy\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.1
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
In [2]:
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"

Matplotlib¶

For this excercise, we have written the following code to load the stock dataset built into plotly express.

In [3]:
stocks = px.data.stocks()
stocks.head()
Out[3]:
date GOOG AAPL AMZN FB NFLX MSFT
0 2018-01-01 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
1 2018-01-08 1.018172 1.011943 1.061881 0.959968 1.053526 1.015988
2 2018-01-15 1.032008 1.019771 1.053240 0.970243 1.049860 1.020524
3 2018-01-22 1.066783 0.980057 1.140676 1.016858 1.307681 1.066561
4 2018-01-29 1.008773 0.917143 1.163374 1.018357 1.273537 1.040708

Question 1:¶

Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.

In [36]:
from matplotlib.pyplot import MultipleLocator
x = stocks['date']
y = stocks['GOOG']
fig, ax = plt.subplots()
ax.plot(x,y)

x_major_locator = MultipleLocator(14)
ax.xaxis.set_major_locator(x_major_locator)

plt.rcParams["figure.figsize"] = (25, 10)

ax.set_title('Google Stock')
ax.set_xlabel('date')
ax.set_ylabel('stock value')

plt.show()
In [4]:
# YOUR CODE HERE

Question 2:¶

You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.

In [35]:
from matplotlib.pyplot import MultipleLocator
x = stocks['date']
y1 = stocks['GOOG']
y2 = stocks['AAPL']
y3 = stocks['AMZN']
y4 = stocks['FB']
y5 = stocks['NFLX']
y6 = stocks['MSFT']

fig, ax = plt.subplots()

ax.plot(x,y1,label = 'GOOG')
ax.plot(x,y2,label = 'AAPL')
ax.plot(x,y3,label = 'AMZN')
ax.plot(x,y4,label = 'FB')
ax.plot(x,y5,label = 'NFLX')
ax.plot(x,y6,label = 'MSFT')

x_major_locator = MultipleLocator(14)
ax.xaxis.set_major_locator(x_major_locator)

plt.rcParams["figure.figsize"] = (20, 10)

ax.set_title('Stocks')
ax.set_xlabel('date')
ax.set_ylabel('stock value')

plt.legend()
plt.show()
In [5]:
# YOUR CODE HERE

Seaborn¶

First, load the tips dataset

In [4]:
tips = sns.load_dataset('tips')
tips.head()
Out[4]:
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4

Question 3:¶

Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.

Some possible questions:

  • Are there differences between male and female when it comes to giving tips?
  • What attribute correlate the most with tip?
In [10]:
# YOUR CODE HERE
# Question: Are there differences between Lunch and Dinner when it comes to giving tips?
g = sns.FacetGrid(tips, col='time', hue='smoker')
g.map(sns.scatterplot, 'total_bill', 'tip')
g.add_legend()
plt.savefig('smoker.png', dpi=200)
plt.show()

Plotly Express¶

Question 4:¶

Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.

The stocks dataset¶

Hints:

  • Turn stocks dataframe into a structure that can be picked up easily with plotly express
In [5]:
# YOUR CODE HERE
df = px.data.stocks() 
fig = px.line(df, x="date", y=["GOOG",'AAPL','AMZN','FB','NFLX','MSFT'])
fig.show()

The tips dataset¶

In [6]:
# YOUR CODE HERE
# Question: Are there differences between Lunch and Dinner when it comes to giving tips?
fig = px.scatter(tips,  #  数据
                 x="total_bill", # xy轴 
                 y="tip", 
                 color="smoker", # 颜色
                 facet_col="time"  # 列方向切面字段
                )
fig.show()

Question 5:¶

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

  • Extract the 2007 year data from the dataframe. You have to process the data accordingly
  • use plotly bar
  • Add different colors for different continents
  • Sort the order of the continent for the visualisation. Use axis layout setting
  • Add text to each bar that represents the population
In [7]:
#load data
df = px.data.gapminder()
df.head()
Out[7]:
country continent year lifeExp pop gdpPercap iso_alpha iso_num
0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG 4
1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG 4
2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG 4
3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG 4
4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG 4
In [8]:
# YOUR CODE HERE
df_2007 = df.query('year == 2007')
df_sum = df_2007.groupby('continent').sum()
fig = px.bar(df_sum, x = "pop",y = df_sum.index,orientation = 'h',color = df_sum.index,text = 'pop')
fig.update_yaxes(categoryorder = "sum ascending")
fig.show()